Navigation device with adaptive fixed pattern noise update and operating method thereof

ABSTRACT

There is provided an optical navigation device including an image sensor and a processing unit. The image sensor outputs successive image frames. The processing unit calculates a contamination level and a motion signal based on filtered image frames, and determines whether to update a fixed pattern noise (FPN) stored in a frame buffer according to a level of FPN subtraction, the calculated contamination level and the calculated motion signal to optimize the update of the fixed pattern noise.

BACKGROUND 1. Field of the Disclosure

This disclosure generally relates to an optical navigation device and,more particularly, to an optical navigation device and an operatingmethod thereof that determine whether to update a fixed pattern noise(FPN) based on a contamination level in images, a level of FPNsubtraction and a motion signal.

2. Description of the Related Art

It is known that the contamination e.g., particles or fingerprint on alens or lens cover, can degrade the motion report of an opticalnavigation device such that accurate motion data may not be able toreported.

Traditionally, a fixed pattern noise can be constructed to be subtractedfrom an image frame to cancel out the influence from contamination thatgenerally forms fixed noises in the image frame. However, if the fixedpattern noise is not constructed properly, not only the noises in acaptured image frame caused by the contamination cannot be correctlyeliminated, but the useful data in the captured image frame is alsoremoved during the calculation such that cursor jitter may be inducedeven though the optical navigation device is stationary.

Accordingly, it is necessary to provide an optical navigation devicethat can build up a proper fixed pattern noise to efficiently eliminatethe influence from contamination accumulated on a lens or lens cover.

SUMMARY

The present disclosure provides an optical navigation device and anoperating method thereof that adopt an adaptive update threshold tooptimize the update of the fixed pattern noise.

The present disclosure provides an optical navigation device includingan image sensor, a frame buffer and a processing unit. The image sensoris configured to capture an image frame. The frame buffer is configuredto store a fixed pattern noise. The processing unit is configured tofilter the image frame to generate a filtered image frame, calculate acontamination level and a first frame summation using the filtered imageframe, generate a denoised image frame using the stored fixed patternnoise and the filtered image frame, calculate a motion signal and asecond frame summation using the denoised image frame, and determinewhether to update the stored fixed pattern noise according to the secondframe summation, the first frame summation and the contamination level.

The present disclosure further provides an operating method of anoptical navigation device. The optical navigation device includes animage sensor, a frame buffer and a processing unit. The operating methodincludes the steps of: capturing an image frame using the image sensor;filtering, by the processing unit, the image frame to generate afiltered image frame; calculating, by the processing unit, acontamination level and a first frame summation using the filtered imageframe; generating a denoised image frame, by the processing unit, usinga fixed pattern noise, which is stored in the frame buffer, and thefiltered image frame; calculating, by the processing unit, a motionsignal and a second frame summation using the denoised image frame; anddetermining, by the processing unit, whether to update the stored fixedpattern noise according to the second frame summation, the first framesummation and the contamination level.

In the optical navigation device and the operating method of the presentdisclosure, multiple update thresholds are previously determined and setin a look up table corresponding to multiple contamination levels. Andone update threshold is selected corresponding to each image framecaptured by the image sensor to prevent from being under-subtracted orover-subtracted by the fixed pattern noise during calculating the motionsignal.

BRIEF DESCRIPTION OF THE DRAWINGS

Other objects, advantages, and novel features of the present disclosurewill become more apparent from the following detailed description whentaken in conjunction with the accompanying drawings.

FIG. 1 is a schematic block diagram of an optical navigation deviceaccording to one embodiment of the present disclosure.

FIG. 2 is an operational schematic diagram of a digital filter of anoptical navigation device according to one embodiment of the presentdisclosure.

FIG. 3 is a look up table indicating the relationship betweencontamination levels and update thresholds according to one embodimentof the present disclosure.

FIG. 4 is an operating method of an optical navigation device accordingto one embodiment of the present disclosure.

FIG. 5 is a schematic of an output track of an optical navigation deviceaccording to one embodiment of the present disclosure showing thefiltered image frame is under-subtracted by FPN.

FIG. 6 is a schematic of an output track of an optical navigation deviceaccording to one embodiment of the present disclosure showing thefiltered image frame is over-subtracted by FPN.

FIG. 7 is a schematic of an output track of an optical navigation deviceaccording to one embodiment of the present disclosure showing thefiltered image frame is properly subtracted by FPN.

DETAILED DESCRIPTION OF THE EMBODIMENT

It should be noted that, wherever possible, the same reference numberswill be used throughout the drawings to refer to the same or like parts.

The optical navigation device of the present disclosure is to optimizethe update of the stored fixed pattern noise (FPN) according to at leasta contamination level as well as a denoised image frame and apre-denoised image frame. In another embodiment, the optical navigationdevice further determines whether to update the stored fixed patternnoise according to a calculated motion signal. In the presentdisclosure, the contamination level is used to determine an updatethreshold, based on a look up table built up previously, to be comparedwith a ratio of frame summations between the denoised image frame andthe pre-denoised image frame. In this way, the pre-denoised image frameis not under-subtracted or over-subtracted by the FPN (illustrated belowby examples) and accurate motion data is reported.

Referring to FIG. 1, it is a schematic block diagram of an opticalnavigation device 100 according to one embodiment of the presentdisclosure. The optical navigation device 100 is used to detect arelative movement with respect to a working surface 90 due to the motionof at least one of the optical navigation device 100 and the workingsurface 90. The optical navigation device 100 includes a light source11, an image sensor 13 and a processing unit 15. The processing unit 15is electrically connected to the light source 11 and the image sensor 13to respectively control the lighting of the light source 11 and theimage capturing of the image sensor 13.

The light source 11 emits light of an identifiable spectrum, e.g.,infrared light, to illuminate the working surface 90, and is controlledby the processing unit 15 to emit light at a predetermined lightingpattern. The light source 11 is a coherent light source, a non-coherentlight source or a partially coherent light source, e.g., a lightemitting diode (LED) or a laser diode (LD).

The image sensor 13 is a charge coupled device (CCD) image sensor, acomplementary metal oxide semiconductor (CMOS) sensor or the like, andhas a pixel array used to capture reflected light from the workingsurface 90 and output an image frame A(x,y). The image frame A(x,y) ispreferably a digital image frame. For example, the image sensor 13further has an analog-to-digital converter (ADC) for converting analograw data of every pixel into digital gray values, or theanalog-to-digital converter is included in the processing unit 15 toperform the analog-to-digital conversion.

The processing unit 15 is a digital signal processor (DSP), amicrocontroller unit (MCU) or an application specific integrated circuit(ASIC), and used to calculate and output a motion signal ΔS with respectto the working surface 90, wherein the motion signal ΔS herein is adenoised signal using a fixed pattern noise (FPN) constructed duringoperation, e.g., each time the optical navigation device 100 is poweredon, in reacting to the accumulated contamination.

The processing unit 15 includes a digital filter 151, a frame buffer152, a subtractor 153, a sum calculator 154, a motion calculator 155 andan update controller 156. It should be mentioned that although FIG. 1shows that different function blocks of the processing unit 15 areseparated from each other, all functions implemented by said differentfunction blocks are considered to be implemented by the processing unit15 using software and/or hardware. The frame buffer 152 is not limitedto be arranged inside the processing unit 15 but an external memorydevice as long as it can be accessed by the processing unit 15.

Referring to FIG. 2, it is an operational schematic diagram of thedigital filter 151 of the optical navigation device 100 according to oneembodiment of the present disclosure.

In the present disclosure, the digital filter 151 filters an image frameA(x,y) to generate a filtered image frame D(x,y). In one non-limitingaspect, the digital filter 151 includes a sum filter and a secondderivative filter. For example, FIG. 2 shows that an image frame A(x,y)outputted by the image sensor 13 has a size of M×M. A 2×2 sum filter isused to perform sum filtering on the whole image frame A(x,y) at first.i.e., totally (M−3)×(M−3) times of sum filtering being performed. Thecalculation of each element of the sum block is shown in FIG. 2 as anexample.

After the sum filtering, a 3×3 second derivative filter is used toperform second derivative filtering on each sum block to generate afiltered image frame D(u,v), which has a size of (M−3)×(M−3). Eachelement Dnn of the filtered image frame D(u,v) is selected from twovalues D_(min) and D_(flat) as show in in FIG. 2. IfD_(max)−D_(min)>(|D_(flat)|>>2), D_(min) is selected as Dnn; otherwise,D_(flat) is selected. |D_(flat)|>>2 means right shifting |D_(flat)| by 2positions, which is equivalent to division of 4. D_(flat) is thesummation of 4 directional filter elements. After the division, D_(flat)will be at the same scale as D_(min) (1 directional filter element).

In the present disclosure, D_(max)=the maximum among |D₀|, |D₉₀|, |D₄₅|and |D₁₃₅| of one sum block; D_(min)=the minimum among |D₀|, |D₉₀|,|D₄₅| and |D₁₃₅| of the one sum block, and the calculation of |D₀|,|D₉₀|, |D₄₅|, |D₁₃₅| and |D_(flat)| is shown in FIG. 2 as an example.Each sum block calculated using the sum filtering obtains one elementDnn of the filtered image frame D(u,v).

The filtered image frame D(u,v) is used to update a fixed pattern noiseF(u,v) stored in the frame buffer 152, and used to be subtracted by apre-updated fixed pattern noise F′(u,v), wherein if the fixed patternnoise F(u,v) is not updated (illustrated below by an example) at oneround, F(u,v)=F′(u,v). In one non-limiting embodiment, an initial fixedpattern noise stored in the frame buffer 152 is a null matrix, but notlimited to. Each element of the initial fixed pattern noise is set as apredetermined value. It is appreciated that since a differentialcalculation is performed between the filtered image frame D(u,v) and thefixed pattern noise F(u,v), the fixed pattern noise F(u,v) is also amatrix having a size of (M−3)×(M−3).

In addition, the processing unit 15 further calculates a contaminationlevel CL and a first frame summation IS₁ using the filtered image frameD(u,v). The first frame summation IS₁ is a sum of values of all elementsof the filtered image frame D(u,v), e.g., D₁₁+D₁₂+ . . .+D_(NN-1)+D_(NN). The contamination level CL is a counting number of aselected value in the filtered image frame D(u,v) generated by thesecond derivative filter, e.g., a number of times that D_(flat) isselected in one filtered image frame D(u,v), and thus CL is smaller thanor equivalent to (M−3)². The contamination level CL represents a levelof contamination, where a lower CL means a higher level ofcontamination, and a higher CL means a lower level of contamination.This is because contamination such as fingerprint on an optical lens orlens cover can increase directional features, which in turn reduce thenumber of times that D_(flat) is selected in one filtered image frameD(u,v).

Then, the processing unit 15 generates a denoised image frame G(u,v)using the fixed pattern noise F′(u,v) and the filtered image frameD(u,v), e.g., subtracting the fixed pattern noise F′(u,v) from thefiltered image frame D(u,v) to generate the denoised image frame G(u,v).The subtraction is performed in an element-by-element manner, i.e.,every element of the fixed pattern noise F(u,v) being subtracted fromthe corresponding element in the filtered image frame D(u,v). Thedenoised image frame G(u,v) is used to calculate a motion signal ΔS bythe motion calculator 155 and calculate a second frame summation IS₂ bythe sum calculator 154. The second frame summation IS₂ is a sum ofvalues of all elements of the denoised image frame G(u,v), which is alsoa matrix having a size of (M−3)×(M−3).

The motion calculator 155 calculates a motion signal ΔS using acorrelation or comparison between two successive denoised image framesG(u,v), wherein the calculation is known to the art and thus detailsthereof are not described herein. The motion signal ΔS herein includesat least one of a displacement, a moving speed and an acceleration ofthe optical navigation device 100 with respect to the working surface90. The motion signal ΔS is sent to the update controller 156 fordetermining whether to update the stored fixed pattern noise F(u,v). Themotion signal ΔS is further outputted to a display device (not shown) ofa computer system for controlling, e.g., a cursor motion shown on ascreen. The motion signal ΔS is further used to perform other controlssuch as page turning, zooming or the like, and not limited tocontrolling the cursor motion.

The update controller 156 determines whether to update the stored fixedpattern noise F(u,v) according to the second frame summation IS₂, thefirst frame summation IS₁ and the contamination level CL, and in otheraspects further according to the motion signal ΔS. A ratio IS₂/IS₁between the second frame summation IS₂ and the first frame summation IS₁represents a level of FPN subtraction, where a lower ratio IS₂/IS₁ meansa higher level of FPN subtraction, and a higher ratio IS₂/IS₁ means alower level of FPN subtraction.

In one non-limiting aspect, the processing unit 15 firstly determines anupdate threshold THu, which is used to be compared with the ratioIS₂/IS₁ for determining whether to update the stored fixed pattern noiseF(u,v), according to the contamination level CL. For example referringto FIG. 3, it is a look up table regarding the relationship betweenmultiple contamination levels CL and multiple update thresholds THupreviously set and stored in a memory of the processing unit 15. Once acurrent contamination level CL is obtained according to the filteredimage frame D(u,v), the processing unit 15 selects a correspondingupdate threshold THu from the look up table.

Besides, the processing unit 15 further compares the calculated motionsignal ΔS with a predetermined motion threshold to determine whether toupdate the stored fixed pattern noise F(u,v). In the present disclosure,the processing unit 15 does not update the stored fixed pattern noiseF(u,v) when any one of the motion signal ΔS and the ratio IS₂/IS₁ issmaller than a corresponding threshold, i.e. the motion signal ΔS beingsmaller than the predetermined motion threshold and/or the ratio IS₂/IS₁being smaller than the selected update threshold THu.

Once the update controller 156 determines that the stored fixed patternnoise F(u,v) should be updated, i.e., both the motion signal ΔS and theratio IS₂/IS₁ being larger than the corresponding threshold, the updatecontroller 156 sends a control signal Cu to the frame buffer 152 toupdate the stored fixed pattern noise F(u,v). In one non-limitingaspect, the processing unit 15 is configured to update the stored fixedpattern noise F(u,v) by calculating a weighted summation of the filteredimage frame D(u,v) and the pre-updated fixed pattern noise F′(u,v). Forexample, F(u,v)=(D(u,v)/K)+(F′(u,v)×(K−1)/K) if F(u,v) is updated,wherein K is a positive integer and indicates a ratio or weight of thecurrent D(u,v) being used to update F(u,v).

Referring to FIG. 4, it is a flow chart of an operating method of anoptical navigation device according to one embodiment of the presentdisclosure, wherein the operating method is adaptable to the opticalnavigation device 100 in FIG. 1. Details of this embodiment aredescribed below using an example.

Step S41: Firstly, the image sensor 13 captures an image frame A(x,y),which is sent to the processing unit 15, having a size of M×M. The imagesensor 13 captures image frames A(x,y) at a frame rate and correspondingto the lighting of the light source 11.

Step S42: The digital filter 151 of the processing unit 15 filters theimage frame A(x,y), for example, using sum filtering and secondderivative filtering to generate a filtered image frame D(u,v) having asize of (M−3)×(M−3). As shown by an example in FIG. 2, the sum filteringis performed using a 2×2 sum filter, and the second derivative filteringis performed using a 3×3 second derivative filter.

Step S43: After the filtered image frame D(u,v) is generated, theprocessing unit 15 calculates a contamination level CL and a first framesummation IS₁ using the filtered image frame D(u,v). The processing unit15 further determines an update threshold THu according to thecalculated contamination level CL, e.g., based on a look up table shownin FIG. 3 or a predetermined relationship. The processing unit 15calculates a sum of values of all elements of the filtered image frameD(u,v) as a first frame summation IS₁.

Step S44: The denoising of the filtered image frame D(u,v) is performedby, for example, subtracting a fixed pattern noise F(u,v), which isstored in the frame buffer 152, from the filtered image frame D(u,v) togenerate a denoised image frame G(u,v). As mentioned above, the fixedpattern noise F(u,v) is a (M−3)×(M−3) matrix to have a same size withthe filtered image frame D(u,v), and an initial fixed pattern noiseoriginally (reset before the Step S41) stored in the frame buffer 152 isset as a null matrix.

The motion calculator 155 of the processing unit 15 calculates a motionsignal ΔS, e.g., using correlation or comparison method, usingsuccessive denoised image frames G(u,v). The sum calculator 154 of theprocessing unit 15 calculates a second frame summation IS₂ of eachdenoised image frames G(u,v), wherein the second frame summation IS₂ isa sum of values of all elements of the denoised image frame G(u,v).

The update controller 156 of the processing unit 15 determines whetherto update the stored fixed pattern noise F(u,v) according to thecontamination level CL, the second frame summation IS₂ and the firstframe summation IS₁ in Steps S46-S47, and in other aspects furtheraccording to the motion signal ΔS in the Step S45. That is, the step S45is optional. It should be mentioned that in FIG. 4 a sequence of theSteps S45 and S461 is exchangeable.

Step 45: The processing unit 15 compares the calculated motion signal ΔSwith a predetermined motion threshold THm (e.g., displacement threshold,speed threshold and/or acceleration threshold), which is determinedaccording to, for example, the noise tolerance and sensitivity of theimage sensor 13. If the motion signal ΔS exceeds (having motion bigenough) the motion threshold THm, then the procedure enters the stepS461. Otherwise, when the motion signal ΔS is smaller than the motionthreshold THm, the processing unit 15 determines that the stored fixedpattern noise F(u,v) is not updated in this round (Step S462) and movesthe operating procedure to the Step S41 to start another update round.

Step S461: The processing unit 15 then compares a ratio IS₂/IS₁ betweenthe second frame summation IS₂ and the first frame summation IS₁ withthe update threshold THu, which is selected according to the calculatedcontamination level CL when the optical navigation device 100 is underoperation. If the ratio IS₂/IS₁ exceeds (e.g., larger than or equal to)the update threshold THu, the processing unit 15 updates the storedfixed pattern noise F(u,v) by calculating a weighted summation of thefiltered image frame D(u,v) and the pre-updated fixed pattern noiseF′(u,v) (Step S47). Otherwise, when the ratio IS₂/IS₁ is smaller thanthe update threshold THu, the processing unit 15 determines that thestored fixed pattern noise F(u,v) is not updated in this round (StepS462) and moves the operating procedure to the Step S41 to start anotherupdate round. If the update is not performed. F(u,v)=F′(u,v) and F(u,v)is subtracted from the filtered image frame D(u,v) in the next updateround.

Finally, the operating procedure returns to the Step S41 to captureanother image frame D(u,v) and start another update round.

In the present disclosure, the FPN update process is always running andit will stop only when the FPN is properly built up. This updatingprocess is governed by steps S45 and S461 in FIG. 4 to avoid over orunder FPN subtraction.

The stored fixed pattern noise F(u,v) is reset to its initial form nexttime before the optical processing unit 15 is used again. In some cases,the user triggers the update procedure shown in FIG. 4 via a userinterface shown on a display device of a computer system or by pressinga predetermined button.

In the present disclosure, it is crucial to control the ratio IS₂/IS₁ atan optimum value. When the ratio IS₂/IS₁ is too high, image frame A(x,y)is under-subtracted for FPN. The optical navigation device 100 is notable to report accurate motion data because useful signal is overwhelmedby strong FPN. FIG. 5 shows a motion track shown on the display when thefiltered image D(u,v) is under-subtracted by the fixed pattern noise.

On the other hand, when the ratio IS₂/IS₁ is too low, image frame A(x,y)is over-subtracted for FPN. This means that useful signal that providesmotion data is also subtracted to cause the optical navigation device100 to report invalid motion data (e.g., cursor shown on the displaydevice being jittery) even while the optical navigation device 100 isstationary. This is result of performing correlation on denoised imageframes G(u,v) with very poor signal to noise ratio. FIG. 6 shows amotion track shown on the display device when the filtered image D(u,v)is over-subtracted by the fixed pattern noise. FIG. 7 shows a motiontrack shown on the display device when the filtered image D(u,v) isproperly subtracted by the fixed pattern noise. FIGS. 5-7 show cursormotion on the display device with the optical navigation device 100being operated to form circular motion.

It should be mentioned that values, e.g., CL, TH_(U), frame rate and Kmentioned in the above embodiments are only intended to illustrate butnot to limit the present disclosure.

As mentioned above, it is crucial to determine a proper fixed patternnoise for obtaining an accurate motion report. Accordingly, the presentdisclosure provides an optical navigation device (e.g., FIG. 1) and anoperating method thereof (e.g., FIG. 4) that determine whether to updatea fixed pattern noise stored in a frame buffer according to acontamination level of a captured image frame to optimize the update ofthe fixed pattern noise thereby reporting accurate motion.

Although the disclosure has been explained in relation to its preferredembodiment, it is not used to limit the disclosure. It is to beunderstood that many other possible modifications and variations can bemade by those skilled in the art without departing from the spirit andscope of the disclosure as hereinafter claimed.

What is claimed is:
 1. An optical navigation device, comprising: animage sensor configured to capture an image frame; a frame bufferconfigured to store a fixed pattern noise; and a processing unitconfigured to filter the image frame using sum filtering and secondderivative filtering to generate a filtered image frame, calculate acontamination level and a first frame summation using the filtered imageframe, wherein the contamination level is a counting number of aselected value in the filtered image frame generated in the secondderivative filtering, and said selected value is a summation of 4directional filter elements in the second derivative filtering, generatea denoised image frame using the stored fixed pattern noise and thefiltered image frame, calculate a motion signal and a second framesummation using the denoised image frame, and determine whether toupdate the stored fixed pattern noise according to the second framesummation, the first frame summation and the contamination level.
 2. Theoptical navigation device as claimed in claim 1, wherein the processingunit is configured to perform the sum filtering using a 2×2 sum filter,and perform the second derivative filtering using a 3×3 secondderivative filter.
 3. The optical navigation device as claimed in claim2, wherein a size of the image frame is M×M, and a size of the filteredimage frame is (M−3)×(M−3).
 4. The optical navigation device as claimedin claim 3, wherein the fixed pattern noise is a (M−3)×(M−3) matrix, andan initial fixed pattern noise stored in the frame buffer is a nullmatrix.
 5. The optical navigation device as claimed in claim 1, whereinthe processing unit is configured to update the stored fixed patternnoise by calculating a weighted summation of the filtered image frameand the stored fixed pattern noise.
 6. The optical navigation device asclaimed in claim 1, wherein the processing unit is further configured todetermine an update threshold according to the contamination level, andcompare a ratio between the second frame summation and the first framesummation with the update threshold.
 7. The optical navigation device asclaimed in claim 6, wherein the processing unit is configured todetermine whether to update the stored fixed pattern noise further bycomparing the motion signal with a predetermined motion threshold. 8.The optical navigation device as claimed in claim 7, wherein theprocessing unit is configured to not update the stored fixed patternnoise when any one of the motion signal and the ratio is smaller thanthe corresponding threshold.
 9. The optical navigation device as claimedin claim 1, wherein the first frame summation is a sum of values of allelements of the filtered image frame; and the second frame summation isa sum of values of all elements of the denoised image frame.
 10. Theoptical navigation device as claimed in claim 1, wherein the motionsignal includes at least one of a displacement, a moving speed and anacceleration of the optical navigation device.
 11. An operating methodof an optical navigation device, the optical navigation devicecomprising an image sensor, a frame buffer and a processing unit, theoperating method comprising: capturing an image frame using the imagesensor; filtering, by the processing unit, the image frame using sumfiltering and second derivative filtering to generate a filtered imageframe; calculating, by the processing unit, a contamination level and afirst frame summation using the filtered image frame, wherein thecontamination level is a counting number of a selected value in thefiltered image frame generated in the second derivative filtering, andsaid selected value is a summation of 4 directional filter elements inthe second derivative filtering; generating a denoised image frame, bythe processing unit, using a fixed pattern noise, which is stored in theframe buffer, and the filtered image frame; calculating, by theprocessing unit, a motion signal and a second frame summation using thedenoised image frame; and determining, by the processing unit, whetherto update the stored fixed pattern noise according to the second framesummation, the first frame summation and the contamination level. 12.The operating method as claimed in claim 11, wherein the sum filteringuses a 2×2 sum filter; and the second derivative filtering uses a 3×3second derivative filter.
 13. The operating method as claimed in claim12, wherein a size of the image frame is M×M, and a size of the filteredimage frame is (M−3)×(M−3).
 14. The operating method as claimed in claim13, wherein the fixed pattern noise is a (M−3)×(M−3) matrix, and aninitial fixed pattern noise stored in the frame buffer is a null matrix.15. The operating method as claimed in claim 11, further comprising:updating the stored fixed pattern noise by calculating a weightedsummation of the filtered image frame and the stored fixed patternnoise.
 16. The operating method as claimed in claim 11, furthercomprising: determining an update threshold according to thecontamination level, and comparing a ratio between the second framesummation and the first frame summation with the update threshold. 17.The operating method as claimed in claim 16, further comprising:comparing the motion signal with a predetermined motion threshold todetermine whether to update the stored fixed pattern noise.
 18. Theoperating method as claimed in claim 17, further comprising: notupdating the stored fixed pattern noise when any one of the motionsignal and the ratio is smaller than the corresponding threshold. 19.The operating method as claimed in claim 11, further comprising:calculating a sum of values of all elements of the filtered image frameas the first frame summation; and calculating a sum of values of allelements of the denoised image frame as the second frame summation.